The Untapped Potential of AI and Machine Learning in Securing Your Document Workflows

Depending on the location and industry, various regulations and models strive to safeguard citizens' sensitive data in specific regions. Disregarding the borders, these regulations include GDPR, HIPAA, CCPA, GLBA, and many more. 

While varying in technical details, these regulations primarily focus on protecting the rights of citizens by penalizing non-compliant organizations that are a risk or already have failed to protect their customers’ sensitive data. 

For example, GDPR believes in data minimization: processing only the necessary data required for a specific purpose. Non-compliance to it might attract fines of up to €20 million or 4% of your organization's worldwide annual revenue from the preceding financial year.

Although not overtly stated, document workflow is one of the most critical elements of data regulation laws. And, with the rise of AI and ML models, it has become necessary to embrace their untapped potential in securing the same. In this article, we’ll discuss what document workflow is, why it needs securing, traditional challenges, and how AI and ML can help you with it. 

What Is Document Workflow?

Every organization needs documents to be circulated within the bounds of the appropriate users to efficiently capture, approve, edit, generate, retain, and destroy the data. A document workflow is a system that ensures proper management of the same.

These documents may include marketing materials, standard operating procedures (SOPs), or highly confidential data such as financial records, trade secrets, and personal information, all of which are vulnerable to unauthorized access and leaks.

To understand the importance of document workflow, let’s discuss an example: 

Imagine you’re a bustling legal firm without a centralized document workflow system. Your file management system relies on scouring the cabinets aided by someone’s nebulous memory. A junior assistant, tasked with managing physical and digital files, accidentally misplaces a crucial contract with a high-profile client. Without a centralized digital document management system, the contract is stored haphazardly among hundreds of other files.

As the deadline for the client’s project approaches, the missing contract goes unnoticed until the client demands a status update. Frantic searches through cluttered filing cabinets and disorganized digital folders yield no results. Your firm's inability to locate the contract in time results in the client losing confidence and withdrawing their business. 

Challenges in Securing Traditional Document Workflows

Legacy document workflows constantly juggle between paper-based documents, manual approvals, limited integration, lack of security, and inefficiency. Let’s dive deeper into discussing challenges in securing the traditional document workflows:

Regulatory Compliances

Regulatory environments frequently change with the constantly evolving threats and new laws, amendments, and guidelines. As an organization using legacy systems, it is challenging to monitor and update your document workflow continuously, increasing the risk of non-compliance and breach.  

Organizations that operate across multiple jurisdictions, each with its own set of ordinances, may find it even more challenging to comply with multiple local regulations simultaneously with a traditional document workflow. Simply, it’s harder to maintain data privacy and protect sensitive data with conventional systems. 

Updated Security Features and Integration

Legacy systems often lack modern security features, making them more susceptible to breaches. Advanced encryption, multi-factor authentication, and other contemporary security protocols might be absent in traditional document workflows. Your vendor may also end support for the system if it’s been a while—withholding updates for known weaknesses. Patching and updating the system may require custom development and could prove to be atypically resource-intensive.

Legacy systems may also lag in terms of integration with modern solutions within the organization, creating a heterogeneous environment. Interoperability issues, such as data format mismatch and dissimilar communication protocols between systems, could occur. 

User Training and Human Error

Users trained in legacy document workflows may not be fully aware of the importance of document security and the potential risks associated with it. The traditional training may also make it challenging for them to adapt to modern systems. Organizations that still rely on legacy workflows may also be resistant to keeping up with the latest security practices and engaging in continuous user training.

As for user error in traditional systems, imagine an administrative assistant accidentally included sensitive HR documents with financial projections for a board meeting, leading to a major breach of trust and data leak. These kinds of human errors often lead to phishing, social engineering, and whaling. 

Managing Paper-Based Documents

As securing storage areas and implementing access control are comparatively more difficult, organizations that predominantly operate through paper-based documents are more prone to theft, loss, and damage. Moreover, paper-based documents can’t be easily encrypted and password-protected, jeopardizing the foundational concept behind sensitive data handling. 

It’s also more cumbersome to manage the lifecycle of paper documents, from creation to disposal. Proper disposal of sensitive documents can be resource-intensive and requires vigilant awareness. 

Remote Access and Collaboration

Remote environments and collaboration are essential in most industries and projects, irrespective of the category of document workflow system. Organizations that rely on legacy workflows, lacking modern security features, often find their data compromised via unsecured networks, use of personal devices, lack of encryption in data transit, and absence of role-based access.    

When it comes to sensitive documents, collaboration can also be threatening if proper training hasn’t been given to the users. Employees might inadvertently share sensitive documents through insecure channels or with unauthorized recipients. Moreover, traditional document workflows often involve physical documents, which can be easily copied, printed, or taken outside the secure perimeter without proper oversight.

Flexibility and Scalability

As mentioned, traditional document workflows often lack the flexibility to adapt to quickly changing security threats and business dynamics. Implementing new security features often causes significant disruption. For instance, your legacy system-dependent company may face flexibility issues with your employees using manual workarounds to satisfy the compliance requirements of a contract, failing frequently. 

Legacy systems can also hinder your organization’s growth by limiting the ability to process a higher volume of documents, manage access for increasing users, and scale physical storage solutions. 

Third-Party Vendors

Third-party vendors may have varying levels of security measures, which can differ significantly from your organization’s standards in the case of legacy workflows. This inconsistency can create vulnerabilities when documents are shared or accessed by these external entities.

When documents are handled by third-party vendors, your organization loses a degree of control over how they are secured and managed. If you haven’t standardized the process for document workflows, this can make it difficult to enforce your security policies and protocols effectively.

How AI And ML Can Resolve the Issues

Some organizations, prevalently in healthcare and education, can’t escape handling legacy documents due to their reliance on long-standing practices, regulatory requirements, and the nature of their operation. Even them, among others, can utilize the development of AI and ML models to generate, retain, process, and destroy documents.

Automated Monitoring

Natural language processing (NLP) and AI are constantly used in medical image analysis for better diagnostic decisions and precision medication. A similar approach can be adopted by various industries to monitor document access attempts, user activity, and document changes within the workflow. This helps identify suspicious behaviors and potential security breaches.  

Audit Trails

The positive impacts of AI and ML on audit quality and efficiency are incontestable. Tailored ML models also can automate the creation and maintenance of comprehensive audit trails for document workflows. This provides a clear record of all actions taken on documents, who performed them, and when, simplifying compliance audits.

Risk Management

AI can analyze historical data and user behavior to identify potential security risks within the workflow. This allows for proactive risk mitigation strategies and prioritization of security measures. Learn more about AI and risk management to comprehend how you may leverage the technology. 

Encryption and Authentication

AI can streamline encryption processes through Neural Cryptography and personalize authentication methods based on user behavior and context, balancing security with user experience.

Automated Patching

As discussed, legacy document workflow systems often lack integration with modern security solutions. Patching a known vulnerability in legacy systems often requires manual and resource-intensive intervention of the IT team. This creates delays and vulnerabilities that the attackers can exploit. 

In the traditional approach, for instance, your IT team would need to be notified of the vulnerability before they research the patch and ensure its compatibility with their legacy system. They would also have to manually install the patch on all servers running the vulnerable component. 

In contrast, an AI-powered approach may involve automating vulnerability detection through continuous database monitoring and identification of appropriate patches for the same. Using built-in checks, the AI can assess the compatibility of the patches with the legacy system and install them automatically, followed by a detailed report. 

Personalized Training

Artificial intelligence can accurately and rapidly identify the strengths and weaknesses of each member of a team and deploy training routines and materials that are more suitable to their pace and learning style. This concludes in much more effective training and saves a lot of valuable employee time. 

Moreover, appropriate AI models can compile learner profiles from multiple sources quickly and more accurately than their human counterparts. 

Intelligent Data Extraction

AI techniques such as NLP can identify and extract structured information from legacy documents. With proper supervision, this can enable automatic extraction of data from invoices, contracts, and forms, dramatically reducing manual efforts and increasing accuracy. AI-driven optical character recognition (OCR) can convert paper documents into digital formats, making them easier to encrypt, search, and manage securely.

Automated Indexing and Version Control

Machine learning can automatically categorize and index digitized documents, ensuring they are stored securely and accessed only by authorized personnel. It enhances the search and retrieval process of the documents based on their content, context, and metadata. It can even return the desired results against vague search terms with more refined AI indexing. 

Secure Disposal

AI can be integrated with document management systems to automate secure document disposal processes based on pre-defined retention policies and regulatory requirements. It can also manage the lifecycle of digital documents, ensuring proper disposal procedures are followed, reducing the risk of breach in both legacy and digital documents. 

Secure Access Management

As mentioned, secure access management is challenging in legacy document workflows. With AI and ML integrated into the system, digitized documents can be managed more securely by ensuring that sensitive data are accessed only by authorized individuals, regardless of their location. 

Collaboration Monitoring

Monitoring every legacy communication channel that shares and processes sensitive data can be difficult. Work emails, for instance, can be a source of insider threat when not monitored and regulated. Trained ML models can accurately detect when sensitive data is transmitted through insecure channels and identify suspicious activity or potential data leaks during document sharing with external parties. 

By detecting certain keywords and patterns, AI can also help minimize regulatory non-compliance. 

Scalable Solutions

AI and ML models can be designed to scale and handle increasing document volumes within the workflow as an organization grows without the need for exponential investment of money and human resources. 

Vendor Risk Assessment

Many legacy system-reliant organizations, especially those that are scaling rapidly, depend on third-party vendors to provide services to manage document workflows. For instance, a legal firm may rely on a third-party vendor for a cloud-based document managemen

t system (DMS), sharing sensitive data with the vendor and introducing potential risks. 

In a traditional approach, the firm’s security team would need to manually research the vendor’s security practices, including their security certifications, documentation, and on-site audit outcomes.

In contrast, the AI-powered approach involves automatic vendor identification, public data collection, gathering breach history, risk scoring, and report generation. Depending on the reports, you may ask for additional security documents, negotiate contractual terms around data security and breaches, or replace the vendor. 

The Bottom Line 

The integration of AI and ML into document workflows holds immense potential for enhancing security, efficiency, and compliance. As organizations grapple with complex regulatory landscapes and the limitations of traditional systems, AI and ML offer transformative solutions that address these challenges head-on.